{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy import genfromtxt\n",
    "from sklearn import linear_model\n",
    "import matplotlib.pyplot as plt  \n",
    "from mpl_toolkits.mplot3d import Axes3D  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 100.     4.     9.3]\n",
      " [  50.     3.     4.8]\n",
      " [ 100.     4.     8.9]\n",
      " [ 100.     2.     6.5]\n",
      " [  50.     2.     4.2]\n",
      " [  80.     2.     6.2]\n",
      " [  75.     3.     7.4]\n",
      " [  65.     4.     6. ]\n",
      " [  90.     3.     7.6]\n",
      " [  90.     2.     6.1]]\n"
     ]
    }
   ],
   "source": [
    "# 读入数据 \n",
    "data = genfromtxt(r\"Delivery.csv\",delimiter=',')\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 100.    4.]\n",
      " [  50.    3.]\n",
      " [ 100.    4.]\n",
      " [ 100.    2.]\n",
      " [  50.    2.]\n",
      " [  80.    2.]\n",
      " [  75.    3.]\n",
      " [  65.    4.]\n",
      " [  90.    3.]\n",
      " [  90.    2.]]\n",
      "[ 9.3  4.8  8.9  6.5  4.2  6.2  7.4  6.   7.6  6.1]\n"
     ]
    }
   ],
   "source": [
    "# 切分数据\n",
    "x_data = data[:,:-1]\n",
    "y_data = data[:,-1]\n",
    "print(x_data)\n",
    "print(y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建模型\n",
    "model = linear_model.LinearRegression()\n",
    "model.fit(x_data, y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coefficients: [ 0.0611346   0.92342537]\n",
      "intercept: -0.868701466782\n",
      "predict: [ 9.06072908]\n"
     ]
    }
   ],
   "source": [
    "# 系数\n",
    "print(\"coefficients:\",model.coef_)\n",
    "\n",
    "# 截距\n",
    "print(\"intercept:\",model.intercept_)\n",
    "\n",
    "# 测试\n",
    "x_test = [[102,4]]\n",
    "predict = model.predict(x_test)\n",
    "print(\"predict:\",predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vLc9Vqo26XMcSIR4fH0dNTQ0cDgcGBgby7pYX62WxkZ1+FEXBZDLBZDKhrq5O/LpwMkIj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\nJYZsgQUWWPA3gYWQLbDAAgv+Jvj/fq5AOXHwpy0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x139acd9ce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.figure().add_subplot(111, projection = '3d') \n",
    "ax.scatter(x_data[:,0], x_data[:,1], y_data, c = 'r', marker = 'o', s = 100) #点为红色三角形  \n",
    "x0 = x_data[:,0]\n",
    "x1 = x_data[:,1]\n",
    "# 生成网格矩阵\n",
    "x0, x1 = np.meshgrid(x0, x1)\n",
    "z = model.intercept_ + x0*model.coef_[0] + x1*model.coef_[1]\n",
    "# 画3D图\n",
    "ax.plot_surface(x0, x1, z)\n",
    "#设置坐标轴  \n",
    "ax.set_xlabel('Miles')  \n",
    "ax.set_ylabel('Num of Deliveries')  \n",
    "ax.set_zlabel('Time')  \n",
    "  \n",
    "#显示图像  \n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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